Abstract
The “food safety” issue has concerned governments from several countries. The accurate monitoring of agriculture have become important specially due to climate change impacts. In this context, the development of new technologies for monitoring are crucial. Finding previously unknown patterns that frequently occur on time series, known as motifs, is a core task to mine the collected data. In this work we present a method that allows a fast and accurate time series motif discovery. From the experiments we can see that our approach is able to efficiently find motifs even when the size of the time series goes longer. We also evaluated our method using real data time series extracted from remote sensing images regarding sugarcane crops. Our proposed method was able to find relevant patterns, as sugarcane cycles and other land covers inside the same area, which are really useful for data analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration.
- 2.
Moderate-Resolution Imaging Spectroradiometer.
References
Catalano, J., Armstrong, T., Oates, T.: Discovering patterns in real-valued time series. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 462–469. Springer, Heidelberg (2006)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 493–498, New York, NY, USA. ACM (2003)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 419–429. Minneapolis, USA (1994)
Goldin, D.Q., Kanellakis, P.C., Kanellakis, P.C.: On similarity queries for time-series data: Constraint specification and implementation. In: Proceedings of the 1st International Conference on Principles and Practice of Constraint Programming, pp. 137–153. Cassis, France (1995)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowl. Inf. Syst. 3, 263–286 (2001)
Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min. Knowl. Disc. 7, 349–371 (2003). Springer
Keogh, E., Lin, J., Lee, S.-H., Herle, H.: Finding the most unusual time series subsequence: algorithms and applications. Knowl. Inf. Syst. 11(1), 1–27 (2007)
Li, Y., Lin, J.: Approximate variable-length time series motif discovery using grammar inference. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, MDMKDD 2010, pp. 10:1–10:9, New York, NY, USA. ACM (2010)
Li, Y., Lin, J., Oates, T.: Visualizing variable-length time series motifs. In: SDM, pp. 895–906. SIAM / Omnipress (2012)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2003, pp. 2–11, New York, NY, USA. ACM (2003)
Lin, J., Keogh, E., Patel, P., Lonardi, S.: Finding motifs in time series. In: The 2nd Workshop on Temporal Data Mining, at the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada. ACM (2002)
Lin, J., Keogh, E.J., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Min. Knowl. Disc. 15, 107–144 (2007)
Mohammad, Y., Nishida, T.: Constrained motif discovery in time series. New Gener. Comput. 27(4), 319–346 (2009)
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W.: Monitoring vegetation systems in the great plains with ERTS. In: Proceedings of the Third ERTS Symposium, Washington, DC, USA, pp. 309–317 (1973)
Udechukwu, A., Barker, K., Alhajj, R.: Discovering all frequent trends in time series. In: Proceedings of the winter international synposium on Information and communication technologies, WISICT 2004, pp. 1–6. Trinity College Dublin (2004)
Wang, L., Chng, E.S., Li, H.: A tree-construction search approach for multivariate time series motifs discovery. Pattern Recogn. Lett. 31(9), 869–875 (2010)
Yankov, D., Keogh, E., Medina, J., Chiu, B., Zordan, V.: Detecting time series motifs under uniform scaling. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, pp. 844–853. ACM, New York, NY, USA (2007)
Acknowledgements
The authors are grateful for the financial support granted by FAPESP, CNPq, CAPES, SticAmsud and Embrapa Agricultural Informatics, Cepagri/Unicamp and Agritempo for data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chino, D.Y.T., Goncalves, R.R.V., Romani, L.A.S., Traina, C., Traina, A.J.M. (2015). Discovering Frequent Patterns on Agrometeorological Data with TrieMotif. In: Cordeiro, J., Hammoudi, S., Maciaszek, L., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2014. Lecture Notes in Business Information Processing, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-22348-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-22348-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22347-6
Online ISBN: 978-3-319-22348-3
eBook Packages: Computer ScienceComputer Science (R0)